Biclustering has become a popular technique for the study of gene expression data,
especially for discovering functionally related gene sets under different subsets of …

Background Biclustering techniques are capable of simultaneously clustering rows and
columns of a data matrix. These techniques became very popular for the analysis of gene …

Biclustering is a powerful data mining technique that allows clustering of rows and columns,
simultaneously, in a matrix-format data set. It was first applied to gene expression data in …

Motivation: In recent years, there have been various efforts to overcome the limitations of
standard clustering approaches for the analysis of gene expression data by grouping genes …

Background In a number of domains, like in DNA microarray data analysis, we need to
cluster simultaneously rows (genes) and columns (conditions) of a data matrix to identify …

A large number of clustering approaches have been proposed for the analysis of gene
expression data obtained from microarray experiments. However, the results from the …

Biclustering extends the traditional clustering techniques by attempting to find (all)
subgroups of genes with similar expression patterns under to-be-identified subsets of …

The need to analyze high-dimension biological data is driving the development of new data
mining methods. Biclustering algorithms have been successfully applied to gene expression …

In a gene expression data matrix, a bicluster is a submatrix of genes and conditions that
exhibits a high correlation of expression activity across both rows and columns. The problem …

Microarrays are one of the latest breakthroughs in experimental molecular biology, which
provide a powerful tool by which the expression patterns of thousands of genes can be …